Judgeman – step-level evals for coding agents, with a trust number (github.com)

Judgeman has introduced a novel evaluation tool for AI coding agents that assesses each step of an agent's run rather than merely the final output. This tool not only flags wasted, repeated, dangerous, or unverified steps but also provides a trust score indicating how closely its assessments align with human judgment. By leveraging existing logs from Claude Code, OpenAI Agents SDK, and other frameworks, Judgeman aims to enhance the reliability of AI-driven coding processes while maintaining an accessible setup through just a few commands in Python. This development is significant for the AI/ML community as it addresses a critical gap in existing evaluation methods by focusing on the step-level analysis, allowing developers to refine agent behavior iteratively. Judgeman's capability to analyze runs from various logging formats enhances its versatility across different AI frameworks. With automated reports and calibrations, it empowers engineers to better understand and improve their coding agents, boosting overall code quality and safety in AI applications. The structured approach to scoring step performance presents potential for advancing the state of AI evaluation methods and trustworthiness in coding environments.